Perhaps you could tell me how a non random event might explain some of the correlations, with no cause and effect relationship.

I have looked at numerous examples of correlations, including those called spurious. They fall into one of the four categories, each and every one. The ones with no cause and effect relationship generally have a small number for n, which explains why random events can create them. When n is larger, there is either no correlation, or it is based on a cause and effect relationship. Apart from a very tiny number of cases. Bear in mind that tossing ten heads is 1 in 1000, but tossing 20 is 1 in a million . The higher the number n might be, the higher the probability that a correlation is not based on chance.

Keep waiting.Journal papers are of research topics. This is not one. I asked for journal papers on aliens from outer space. If they existed, scientists would be studying them. Your plaints about journal papers are inappropriate for this subject, and sound a bit like Gorgeous demanding proof I exist.

Or else you need to use scientific backing to prove me wrong. Since I am not wrong, that is not possible. Random events by their very nature, are not predictable. You made a statement that most correlations had nothing to do with cause and effect, and another statement that correlations were able to be used to make predictions. Your two statements are mutually contradictory.

If a correlation is based on cause and effect, even if the cause is a third variable, then the results will be consistent and it can be used for predictions. If a correlation is an accident, based on random changes in the variables, then, like the coin tosses, it cannot be used to predict future events.

The definition of random is to permit all possible outcomes to be equally probable. If all outcomes are equally probable, then you cannot predict those outcomes. Simple really.

Your claim is not science. Its what the definition of causation or correlation is...or however you want to slice it. thats not science but rather sociology. Truth being a social construct.....just different degrees of objectivity and confirmation..... but its not "science."

Real Name: bobbo the existential pragmatic evangelical anti-theist and Class Warrior.Asking: What is the most good for the most people?Sample Issue: Should the Feds provide all babies with free diapers?

What I am saying, Bobbo, fits with what we know of reality. Xouper has made some nonsense statements about using what he claims are accidental correlations for prediction. If truly accidental, meaning based on random juxtapositions of numbers, then prediction is impossible.

To get an ongoing correlation, with n as a large number, requires something to drive the correlation, not just randomness. The only thing that can drive such a correlation is a cause and effect relationship.

I suspect that Xouper already knows this, but be is a stubborn cuss and refuses to admit being wrong.

Lance Kennedy wrote:What I am saying, Bobbo, fits with what we know of reality. Xouper has made some nonsense statements about using what he claims are accidental correlations for prediction. If truly accidental, meaning based on random juxtapositions of numbers, then prediction is impossible.

To get an ongoing correlation, with n as a large number, requires something to drive the correlation, not just randomness. The only thing that can drive such a correlation is a cause and effect relationship.

I suspect that Xouper already knows this, but be is a stubborn cuss and refuses to admit being wrong.

Lance Kennedy wrote:What I am saying, Bobbo, fits with what we know of reality. Xouper has made some nonsense statements about using what he claims are accidental correlations for prediction. If truly accidental, meaning based on random juxtapositions of numbers, then prediction is impossible.

To get an ongoing correlation, with n as a large number, requires something to drive the correlation, not just randomness. The only thing that can drive such a correlation is a cause and effect relationship.

I suspect that Xouper already knows this, but be is a stubborn cuss and refuses to admit being wrong.

Yes that is what you said. but the subject is whether or not the understanding of correlation to causation is "science". Of course, its definitional....and I think its definitional as a product of general agreement rather than science per se meaning "testable." You don't test the definition of correlation. too close to cipher?

Real Name: bobbo the existential pragmatic evangelical anti-theist and Class Warrior.Asking: What is the most good for the most people?Sample Issue: Should the Feds provide all babies with free diapers?

The main evidence for what I am saying is examples. I checked out the web site for "spurious " correlations, and found that all of the ones based on accident have a very low number for n. My assertion is that if a correlation is truly accidental, n must be small. If a correlation is to hold over a large value of n, then it must have something more substantial behind it than accident. That something will be a cause and effect relationship.

The evidence for this is the correlations themselves. Truly accidental correlations will have low values for n. Those with high values of n will be related causally, whether directly or through a third factor.

In this wiki account, it is implied that spurious correlations will cause error 5% of the time.

I quote .

" While a true null hypothesis will be accepted 95% of the time, the other 5% of the times having a null hypothesis of no correlation a zero correlation will be wrongly rejected, causing acceptance of a correlation which is spurious. "

In this wiki account, it is implied that spurious correlations will cause error 5% of the time.

Sorry, Lance, but that is not what it says. Not even close.

It says that IF the following conditions are true:

The significance level is specified to be 5%

There is in fact no correlation between X and Y (i.e. the null hypothesis is true)

The data are such that a spurious correlation can accidentally occur 5% of the time.

Then 5% of the time it will be incorrectly assumed that a correlation exists when in fact it doesn't.

That is not at all the same thing as your blanket claim that all spurious correlations will cause an error 5% of the time.

Consider what happens with a different third condition. IF the following conditions are true:

The significance level is specified to be 5%

There is in fact no correlation between X and Y (i.e. the null hypothesis is true)

The data are such that a spurious correlation can accidentally occur only 1% of the time.

Then one percent of the time, it will be incorrectly assumed that a correlation exists when in fact it doesn't.

That is not at all the same thing as your blanket claim that all spurious correlations will cause an error 5% of the time.

What that article says is IF the null hypothesis is true (i.e. there is no correlation) and IF the data is such that a spurious correlation can happen by accident N% of the time, then a correlation will be incorrectly assumed N% of the time, that is, there will be a false positive N% of the time.

That is not at all the same thing as your blanket claim that all spurious correlations will cause an error 5% of the time.

In any case, what does any of that have to do with any of your claims in this thread?

Also, perhaps what's more interesting about that wiki article is that it refutes one of your other prized claims.

That wiki article clearly says that if X and Y are correlated where X does not cause Y and Y does not cause X, and if there is a third, confounding variable W such that W causes X and W causes Y, then they say there is no causal relation between X and Y, and that the correlation between X and Y is a spurious correlation.

That's what I have been saying all along.

It specifically mentions the correlation between ice cream sales and drownings in swimming pools and says that is an example of a "spurious correlation" that has no "causal relation", even though there might be a third, confounding variable.

The term 'spurious ' does not mean no cause and effect. It just means that variables A and B do not cause each other. But if variable C causes A and B, that is still cause and effect. Yet this is called spurious. Which is just semantics at work misleading. A third confounding variable is still a causal relation. It is just indirect. It all boils down to which words you use. In your case, words used to hide the underlying reality, which is still cause and effect.

Incidentally, I used the example once before of correlations between prokaryotes and other life forms to show that there are an astronomical number of cause and effect correlations, far outnumbering accidental correlations. In fact, I discovered today that I was way too conservative. The latest New Scientist (16 Sept. 2017, page 6) had an update on the estimated number of prokaryotes species. It is in excess of a trillion ! If each such species interacts causally with only ten other species or non biotic factors (temperature, pressure, chemicals, light etcetra), which is enormously conservative, there will be at least ten trillion causal correlations. Just for prokaryotes. There is no way in the world that accidental correlations can come within orders of magnitude of causal correlations in number.

The overwhelming majority of strong correlations are based on a cause and effect relationship.

The term 'spurious ' does not mean no cause and effect. It just means that variables A and B do not cause each other. But if variable C causes A and B, that is still cause and effect. Yet this is called spurious. Which is just semantics at work misleading. A third confounding variable is still a causal relation. It is just indirect. It all boils down to which words you use. In your case, words used to hide the underlying reality, which is still cause and effect.

I am not trying to hide anything.

My point here is to use the scientific jargon correctly, which you are not doing. Using the jargon correctly does not preclude discussing the "underlying reality".

Fact: The wikipedia article you cited does not support your personal definitions, in fact just the opposite.

Why can't you just agree to use the scientific jargon the way it is defined by the scientific community and stop all this nonsense about using your own personal jargon? What do you hope to gain by persistently refusing to use the scientific jargon correctly?

Lance Kennedy wrote:Incidentally, I used the example once before of correlations between prokaryotes and other life forms to show that there are an astronomical number of cause and effect correlations, far outnumbering accidental correlations.

. . . The overwhelming majority of strong correlations are based on a cause and effect relationship.

Where is your journal paper for that scientific claim?

I have asked you repeatedly for that evidence and you continue to avoid giving it, and instead you merely repeat your claim without evidence.

As I have said previously, I accept that you have shown a large number for K. But you have not yet shown that K is greater than N.

To refresh your memory:

K is the number of strong correlations that also have a "causal relation".N is the number of strong correlations that do not have a "causal relation".

And by "causal relation", I am using the same definition as the scientific community (which is also in the wikipedia article you cited), and not your personal definition.

I have given heaps of evidence. It is just convenient for you not to recognise it as evidence. Real world examples are evidence, if they are present in sufficient number to be statistically significant. Ten trillion is probably statistically significant.

And jargon is counter to good science if it is used to hide the truth, as you are doing. Most strong correlations are causal, if you get past your misleading use of the term 'spurious ', and accept that when a third factor CAUSES the first two factors, that is causal. Scientific jargon should be used to illustrate truth. You are using it as a smoke screen to hide the truth.

Lance, here's a correlation: the more times you give the same explanation and it is rejected, the less likely you are to have a meaningful conversation.

Real Name: bobbo the existential pragmatic evangelical anti-theist and Class Warrior.Asking: What is the most good for the most people?Sample Issue: Should the Feds provide all babies with free diapers?

I must admit that I find arguing with Xouper very frustrating. He is requiring the whole idea of correlations to be meaningless, but is denying that they are meaningless, despite the fact that his whole argument forces them to be meaningless.

My thesis is that a correlation increases the probability of two variables being related by cause and effect*. He denies this. Such a denial makes the correlation pointless, and would require that scientists who spend lots of time, money, and effort hunting correlations, to be wasting all three. When asked how correlations have meaning if they are not cause and effect, he says predictions. Yet a correlation that occurs by chance cannot be used for making predictions. He denies this also.

To cover up his lack of logic and his lack of truth, he quotes jargon, and tries to tell me I am wrong because I go straight to the underlying truth instead of arguing terminology.

*Cause and effect includes the situation where a third variable is the source of the cause.

Yes, its a weird dance both of you are engaged in. I don't see the pleasure either of you can/should get from it..... yet the dance goes on.

Real Name: bobbo the existential pragmatic evangelical anti-theist and Class Warrior.Asking: What is the most good for the most people?Sample Issue: Should the Feds provide all babies with free diapers?

Lance Kennedy wrote:I have given heaps of evidence. It is just convenient for you not to recognise it as evidence. Real world examples are evidence, if they are present in sufficient number to be statistically significant. Ten trillion is probably statistically significant.

I have accepted your estimate for K (being very large), but you have not yet given any evidence whatsoever that K > N.

Lance Kennedy wrote:And jargon is counter to good science if it is used to hide the truth, as you are doing.

BS. I am not hiding anything. We are talking about it here. It is not hidden.

There is a reason for scientific jargon. You do not have the authority to redefine it as you keep trying to do.

This conversation is going nowhere and is getting tiresome.

The bottom line is, you haven't made your case, despite repeated requests.

Lance Kennedy wrote:I must admit that I find arguing with Xouper very frustrating. He is requiring the whole idea of correlations to be meaningless, but is denying that they are meaningless, despite the fact that his whole argument forces them to be meaningless.

That's BS.

And you know it.

Stop lying about my position.

Lance Kennedy wrote:My thesis is that a correlation increases the probability of two variables being related by cause and effect*. He denies this.

I have repeatedly asked for a journal paper that supports that claim and you have repeatedly refused to cite one.

Your claim is hereby rejected as unfounded for lack of evidence that meets your own personal standard.

The evidence comes from the trillions of examples to support my case, versus the few thousand you mentioned to support yours.

There will be no scientific paper on this, for the simple reason that it is impossible to quantify all correlations. When there are trillions, it cannot be done. Your demand is like asking for a scientific paper to prove the universe is finite. It cannot be done, because the universe is too big to be measured. Instead, we are left to rely on logic (mine) versus unsubstantiated speculation (yours).

Yet your argument includes the suggestion that most correlations are accidental, and yet most correlations can be used for predictions.

An accidental correlation comes from two sets of numbers that accidentally or randomly are in relation to each other. A random sequence of numbers cannot be used to make predictions. Yet you think all those accidental correlations can be used to make predictions.

The truth is that ONLY correlations which are based on a cause and effect relationship can be used to make reliable predictions. A cause and effect relationship includes those correlations where a third variable influences the two variables that correlate. Accidental correlations are based on a random juxtaposition of two series of numbers. Being random, they cannot be used for predictions.

You are big on respecting jargon. I suspect that your feeling for jargon is over the top, and you cannot see past the jargon. In this case, because the third type of correlation (with a third variable influencing the other two) is called spurious using jargon, you cannot see that it too is based on causation, if indirect.

If C causes A, and D causes B, then if A and B correlated, then so must C and D. What links C and D ? It will be a causation relationship.

This is not unusual. For example, did you know that beer drinking correlates ( though not strongly) with lung cancer. There is a C and D involved.

Men in lower socio-economic groups are more likely to drink beer than wine. So beer drinking correlates with their social grouping.Lung cancer correlates with smoking. So what links beer and lung cancer ? Smoking also correlates with the lower socio-economic group.

So in addition to the variables of beer drinking and lung cancer, we have the added variables of smoking and socio-economic group. Four variables creating a correlation. But a definite causation leads to that correlation. It is a psychological cause, but definitely a cause. It is not an accident.

Lance Kennedy wrote:If C causes A, and D causes B, then if A and B correlated, then so must C and D. What links C and D ? It will be a causation relationship.

Maybe so, maybe not.

It is not always the case that a causal relation also has a correlation.

For example, if C causes A, then it is not always the case that C and A are correlated. The correlation coefficient between C and A could be near zero.

Secondly, even if C and A are correlated, and D and B are correlated, the two correlations could be sufficiently different such that the correlation coefficient between C and D could be small enough to be considered not correlated.

As a mathematician might say it, correlation is not a transitive function.

Lance Kennedy wrote:The Fibonacci sequence is very familiar to biologists and my degree is in biology (eg the shape of a nautilus shell). But it is definitely causal, created by evolution for the maximum efficiency.

[Sidebar: I chose Fibonacci because I assumed you'd be familiar with it, thus saving reams of paper explaining it.]

Suppose we have a variable A that has data (a list of numbers) proportional to the Fibonacci sequence, then what exactly are you saying is the cause of A?

Are you saying "evolution" causes A?

I can accept there are many examples of that in biology, but then here's my next question:

If that is what you are saying, then please show how to compute the correlation coefficient between "evolution" and the variable A?

What list of numbers are you using for "evolution" such that when there is more "evolution", there is more A?

Accidental correlations do happen. I believe though, from experience, that the majority of strong correlations are causal.

On the Fibonacci series.Take the nautilus shell, which is perhaps the most beautiful example. That is due to the rate of growth of the nautilus animal. Rate of growth tends to be exponential in all species. A bacterial population , which under ideal conditions, will double every 30 minutes, generates a parabolic graph. The body of the nautilus animal also grows exponentially, but it's increase in volume approximates a Fibonacci series. The shell grows in order to accommodate the larger body, and develops in the same pattern, resulting eventually in that gorgeous shape.

There was an article in New Scientist several years ago describing the exact reason why this form of exponential growth became a Fibonacci series. To be frank, I did not pay enough attention to it, since I was more interested in the biology than the math. Definitely causal, though. The same general pattern is found in many other animals, for the same reasons, though none as beautiful as the nautilus shell.

Lance Kennedy wrote:On the Fibonacci series.Take the nautilus shell, which is perhaps the most beautiful example. That is due to the rate of growth of the nautilus animal. Rate of growth tends to be exponential in all species. A bacterial population , which under ideal conditions, will double every 30 minutes, generates a parabolic graph. The body of the nautilus animal also grows exponentially, but it's increase in volume approximates a Fibonacci series. The shell grows in order to accommodate the larger body, and develops in the same pattern, resulting eventually in that gorgeous shape.

There was an article in New Scientist several years ago describing the exact reason why this form of exponential growth became a Fibonacci series. To be frank, I did not pay enough attention to it, since I was more interested in the biology than the math. Definitely causal, though. The same general pattern is found in many other animals, for the same reasons, though none as beautiful as the nautilus shell.

I agree there is an explanation for the growth rate. I do not dispute that. It is not random, which is why I chose this example in the first place.

However, none of that answers my question: How do you compute the correlation coefficient between the cause and the effect?

Or do you agree that even though "evolution" causes the growth rate, that there is no actual correlation between "evolution" and that growth rate?

Keep in mind that in order to compute a correlation, there must be two lists of numbers. Of course, you already know that, but I just want to make it explicit what we are talking about.

One variable, let's call it G for the growth rate of a Nautilus shell, has a list of numbers that is proportional to the Fibonacci sequence.

The other variable, let's call it E for evolution, has a list of numbers that represents, what? I don't know what you have in mind for that, so that's why I ask.

xouper wrote: Or do you agree that even though "evolution" causes the growth rate, that there is no actual correlation between "evolution" and that growth rate?

Evolution is not a cause, it is an effect. Such a basic misconception. How did that happen?????

xxxx

xouper wrote: The other variable, let's call it E for evolution, has a list of numbers that represents, what? I don't know what you have in mind for that, so that's why I ask.

Please show how G and E are correlated, or not.

EVERY list of numbers correlates to EVERY other list of numbers. from 1.0 to -1.0. So....E might very highly correlate to the number of hotdogs sold since 1900. THE KEY IS: is there any testable cause and effect relationship that can be proposed?

Vacuous.

Real Name: bobbo the existential pragmatic evangelical anti-theist and Class Warrior.Asking: What is the most good for the most people?Sample Issue: Should the Feds provide all babies with free diapers?

xouper wrote: Or do you agree that even though "evolution" causes the growth rate, that there is no actual correlation between "evolution" and that growth rate?

Evolution is not a cause, it is an effect. Such a basic misconception. How did that happen?????

Well, that is a very good question. Why don't you ask Lance that question since he is the one who originally proposed that evolution is the cause.

bobbo_the_Pragmatist wrote:

xouper wrote: The other variable, let's call it E for evolution, has a list of numbers that represents, what? I don't know what you have in mind for that, so that's why I ask.

Please show how G and E are correlated, or not.

EVERY list of numbers correlates to EVERY other list of numbers. from 1.0 to -1.0.

If the coefficient is near zero, then there is no correlation, by definition.

bobbo_the_Pragmatist wrote: So....E might very highly correlate to the number of hotdogs sold since 1900. THE KEY IS: is there any testable cause and effect relationship that can be proposed?

Vacuous.

Again, good point. Please ask Lance to explain why he thinks there is a strong correlation between evolution and a growth rate that can be modeled with a Fibonacci sequence. I don't know what he had in mind when he made that claim, so I am merely asking questions trying to understand what he meant.

Secondly, if there is a strong correlation between two variables with a Fibonacci-like growth rate, and as you say, there is no testable cause and effect relationship, then that is the kind of vacuous example I have been using to explain to Lance how there can be such a correlation without a cause.

Real Name: bobbo the existential pragmatic evangelical anti-theist and Class Warrior.Asking: What is the most good for the most people?Sample Issue: Should the Feds provide all babies with free diapers?